Towards a less conservative analysis of geometric tolerances
Bibliographic record
Abstract
The statistical analysis of geometric tolerances has been carried out, traditionally, by using either the Taylor series method or the Monte Carlo simulation. Although the Taylor series method is fast, it is not capable of analysing many highly non-linear cases of geometric tolerances, especially when several of them are specified for the same feature. Hence, the Monte Carlo simulation remains the most widely used method for the statistical analysis of geometric tolerances. Similarly to other methods, during each step of the Monte Carlo simulation, all features in the assembly are generated including random variations due to the manufacturing processes’ capabilities. If one of the features does not fall within the specified tolerance range, the whole instance of parts (i.e. the whole assembly) is rejected. This simulation is more conservative than a real assembly-inspection process. This paper presents an augmented Monte Carlo simulation in which assemblies are not rejected if one or more parts are rejected while other parts are within specifications. Instead, the in-spec parts are re-grouped with other acceptable parts of other simulations. This emulates the concept of parts interchangeability in real assembly processes. The results obtained by using the proposed approach are compared with those obtained from a standard Monte Carlo simulation tolerance analysis to demonstrate that the latter is unnecessarily conservative.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".